<<<<<<< Updated upstream Pandas Profiling Report

Overview

Dataset statistics

Number of variables28
Number of observations15848
Missing cells15848
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 MiB
Average record size in memory224.0 B

Variable types

Categorical11
DateTime1
TimeSeries13
Numeric3

Alerts

State Code has constant value "4" Constant
County Code has constant value "19" Constant
Site Num has constant value "1011" Constant
Address has constant value "1237 S. BEVERLY , TUCSON" Constant
State has constant value "Arizona" Constant
County has constant value "Pima" Constant
City has constant value "Tucson" Constant
NO2 Units has constant value "Parts per billion" Constant
O3 Units has constant value "Parts per million" Constant
SO2 Units has constant value "Parts per billion" Constant
CO Units has constant value "Parts per million" Constant
SO2 AQI is highly correlated with SO2 1st Max ValueHigh correlation
CO 1st Max Value is highly correlated with NO2 AQI and 2 other fieldsHigh correlation
CO 1st Max Hour is highly correlated with NO2 1st Max HourHigh correlation
State Code is highly correlated with Site Num and 9 other fieldsHigh correlation
County Code is highly correlated with State Code and 9 other fieldsHigh correlation
Site Num is highly correlated with State Code and 9 other fieldsHigh correlation
Address is highly correlated with State Code and 9 other fieldsHigh correlation
State is highly correlated with State Code and 9 other fieldsHigh correlation
County is highly correlated with State Code and 9 other fieldsHigh correlation
City is highly correlated with State Code and 9 other fieldsHigh correlation
NO2 Units is highly correlated with State Code and 9 other fieldsHigh correlation
O3 Units is highly correlated with State Code and 9 other fieldsHigh correlation
SO2 Units is highly correlated with State Code and 9 other fieldsHigh correlation
CO Units is highly correlated with State Code and 9 other fieldsHigh correlation
NO2 Mean is highly correlated with NO2 1st Max Value and 4 other fieldsHigh correlation
NO2 1st Max Value is highly correlated with NO2 Mean and 3 other fieldsHigh correlation
NO2 AQI is highly correlated with NO2 1st Max Hour and 4 other fieldsHigh correlation
O3 Mean is highly correlated with O3 1st Max Value and 1 other fieldsHigh correlation
O3 1st Max Value is highly correlated with NO2 1st Max Hour and 4 other fieldsHigh correlation
O3 AQI is highly correlated with NO2 1st Max Hour and 4 other fieldsHigh correlation
SO2 Mean is highly correlated with SO2 1st Max Value and 1 other fieldsHigh correlation
SO2 1st Max Value is highly correlated with O3 1st Max Hour and 4 other fieldsHigh correlation
SO2 1st Max Hour is highly correlated with SO2 1st Max ValueHigh correlation
CO Mean is highly correlated with NO2 Mean and 2 other fieldsHigh correlation
CO AQI is highly correlated with NO2 1st Max Hour and 5 other fieldsHigh correlation
NO2 1st Max Hour is highly correlated with NO2 AQI and 5 other fieldsHigh correlation
O3 1st Max Hour is highly correlated with NO2 1st Max Hour and 3 other fieldsHigh correlation
SO2 AQI has 7924 (50.0%) missing values Missing
CO AQI has 7924 (50.0%) missing values Missing
NO2 Mean is non stationary Non stationary
NO2 1st Max Value is non stationary Non stationary
NO2 1st Max Hour is non stationary Non stationary
NO2 AQI is non stationary Non stationary
O3 Mean is non stationary Non stationary
O3 1st Max Value is non stationary Non stationary
O3 1st Max Hour is non stationary Non stationary
O3 AQI is non stationary Non stationary
SO2 Mean is non stationary Non stationary
SO2 1st Max Value is non stationary Non stationary
SO2 1st Max Hour is non stationary Non stationary
CO Mean is non stationary Non stationary
CO AQI is non stationary Non stationary
NO2 Mean is seasonal Seasonal
NO2 1st Max Value is seasonal Seasonal
NO2 1st Max Hour is seasonal Seasonal
NO2 AQI is seasonal Seasonal
O3 Mean is seasonal Seasonal
O3 1st Max Value is seasonal Seasonal
O3 1st Max Hour is seasonal Seasonal
O3 AQI is seasonal Seasonal
SO2 Mean is seasonal Seasonal
SO2 1st Max Value is seasonal Seasonal
SO2 1st Max Hour is seasonal Seasonal
CO Mean is seasonal Seasonal
CO AQI is seasonal Seasonal
NO2 1st Max Hour has 1628 (10.3%) zeros Zeros
SO2 Mean has 678 (4.3%) zeros Zeros
SO2 1st Max Value has 678 (4.3%) zeros Zeros
SO2 1st Max Hour has 1930 (12.2%) zeros Zeros
SO2 AQI has 456 (2.9%) zeros Zeros
CO 1st Max Hour has 4740 (29.9%) zeros Zeros

Reproduction

Analysis started2022-10-20 17:50:24.840415
Analysis finished2022-10-20 17:50:53.268841
Duration28.43 seconds
Software versionpandas-profiling v3.3.1
Download configurationconfig.json

Variables

State Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
4
15848 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15848
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
415848
100.0%

Length

2022-10-20T18:50:53.365564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Overview

Dataset statistics

Number of variables28
Number of observations15848
Missing cells15848
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 MiB
Average record size in memory224.0 B

Variable types

Categorical11
DateTime1
TimeSeries13
Numeric3

Alerts

State Code has constant value "4" Constant
County Code has constant value "19" Constant
Site Num has constant value "1011" Constant
Address has constant value "1237 S. BEVERLY , TUCSON" Constant
State has constant value "Arizona" Constant
County has constant value "Pima" Constant
City has constant value "Tucson" Constant
NO2 Units has constant value "Parts per billion" Constant
O3 Units has constant value "Parts per million" Constant
SO2 Units has constant value "Parts per billion" Constant
CO Units has constant value "Parts per million" Constant
SO2 AQI is highly correlated with SO2 1st Max ValueHigh correlation
CO 1st Max Value is highly correlated with NO2 AQI and 2 other fieldsHigh correlation
CO 1st Max Hour is highly correlated with NO2 1st Max HourHigh correlation
State Code is highly correlated with Address and 9 other fieldsHigh correlation
County Code is highly correlated with Address and 9 other fieldsHigh correlation
Site Num is highly correlated with Address and 9 other fieldsHigh correlation
Address is highly correlated with State and 9 other fieldsHigh correlation
State is highly correlated with Address and 9 other fieldsHigh correlation
County is highly correlated with Address and 9 other fieldsHigh correlation
City is highly correlated with Address and 9 other fieldsHigh correlation
NO2 Units is highly correlated with Address and 9 other fieldsHigh correlation
O3 Units is highly correlated with Address and 9 other fieldsHigh correlation
SO2 Units is highly correlated with Address and 9 other fieldsHigh correlation
CO Units is highly correlated with Address and 9 other fieldsHigh correlation
NO2 Mean is highly correlated with NO2 1st Max Value and 4 other fieldsHigh correlation
NO2 1st Max Value is highly correlated with NO2 Mean and 3 other fieldsHigh correlation
NO2 AQI is highly correlated with NO2 1st Max Hour and 4 other fieldsHigh correlation
O3 Mean is highly correlated with O3 1st Max Value and 1 other fieldsHigh correlation
O3 1st Max Value is highly correlated with NO2 1st Max Hour and 4 other fieldsHigh correlation
O3 AQI is highly correlated with NO2 1st Max Hour and 4 other fieldsHigh correlation
SO2 Mean is highly correlated with SO2 1st Max Value and 1 other fieldsHigh correlation
SO2 1st Max Value is highly correlated with O3 1st Max Hour and 4 other fieldsHigh correlation
SO2 1st Max Hour is highly correlated with SO2 1st Max ValueHigh correlation
CO Mean is highly correlated with NO2 Mean and 2 other fieldsHigh correlation
CO AQI is highly correlated with NO2 1st Max Hour and 5 other fieldsHigh correlation
NO2 1st Max Hour is highly correlated with NO2 AQI and 5 other fieldsHigh correlation
O3 1st Max Hour is highly correlated with NO2 1st Max Hour and 3 other fieldsHigh correlation
SO2 AQI has 7924 (50.0%) missing values Missing
CO AQI has 7924 (50.0%) missing values Missing
NO2 Mean is non stationary Non stationary
NO2 1st Max Value is non stationary Non stationary
NO2 1st Max Hour is non stationary Non stationary
NO2 AQI is non stationary Non stationary
O3 Mean is non stationary Non stationary
O3 1st Max Value is non stationary Non stationary
O3 1st Max Hour is non stationary Non stationary
O3 AQI is non stationary Non stationary
SO2 Mean is non stationary Non stationary
SO2 1st Max Value is non stationary Non stationary
SO2 1st Max Hour is non stationary Non stationary
CO Mean is non stationary Non stationary
CO AQI is non stationary Non stationary
NO2 Mean is seasonal Seasonal
NO2 1st Max Value is seasonal Seasonal
NO2 1st Max Hour is seasonal Seasonal
NO2 AQI is seasonal Seasonal
O3 Mean is seasonal Seasonal
O3 1st Max Value is seasonal Seasonal
O3 1st Max Hour is seasonal Seasonal
O3 AQI is seasonal Seasonal
SO2 Mean is seasonal Seasonal
SO2 1st Max Value is seasonal Seasonal
SO2 1st Max Hour is seasonal Seasonal
CO Mean is seasonal Seasonal
CO AQI is seasonal Seasonal
NO2 1st Max Hour has 1628 (10.3%) zeros Zeros
SO2 Mean has 678 (4.3%) zeros Zeros
SO2 1st Max Value has 678 (4.3%) zeros Zeros
SO2 1st Max Hour has 1930 (12.2%) zeros Zeros
SO2 AQI has 456 (2.9%) zeros Zeros
CO 1st Max Hour has 4740 (29.9%) zeros Zeros

Reproduction

Analysis started2022-10-20 18:29:29.292192
Analysis finished2022-10-20 18:29:47.437858
Duration18.15 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

State Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
4
15848 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15848
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
415848
100.0%

Length

2022-10-20T19:29:47.496924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:50:53.499687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:29:47.577679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
415848
100.0%

Most occurring characters

ValueCountFrequency (%)
415848
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15848
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
415848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15848
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
415848
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
415848
100.0%

County Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
19
15848 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters31696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row19
2nd row19
3rd row19
4th row19
5th row19

Common Values

ValueCountFrequency (%)
1915848
100.0%

Length

2022-10-20T18:50:53.619978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
415848
100.0%

Most occurring characters

ValueCountFrequency (%)
415848
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15848
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
415848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15848
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
415848
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
415848
100.0%

County Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
19
15848 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters31696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row19
2nd row19
3rd row19
4th row19
5th row19

Common Values

ValueCountFrequency (%)
1915848
100.0%

Length

2022-10-20T19:29:47.643958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:50:53.760672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:29:47.715593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1915848
100.0%

Most occurring characters

ValueCountFrequency (%)
115848
50.0%
915848
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number31696
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
115848
50.0%
915848
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common31696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
115848
50.0%
915848
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII31696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115848
50.0%
915848
50.0%

Site Num
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
1011
15848 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters63392
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1011
2nd row1011
3rd row1011
4th row1011
5th row1011

Common Values

ValueCountFrequency (%)
101115848
100.0%

Length

2022-10-20T18:50:54.014197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1915848
100.0%

Most occurring characters

ValueCountFrequency (%)
115848
50.0%
915848
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number31696
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
115848
50.0%
915848
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common31696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
115848
50.0%
915848
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII31696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115848
50.0%
915848
50.0%

Site Num
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
1011
15848 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters63392
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1011
2nd row1011
3rd row1011
4th row1011
5th row1011

Common Values

ValueCountFrequency (%)
101115848
100.0%

Length

2022-10-20T19:29:47.899897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:50:54.144990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:29:47.976045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
101115848
100.0%

Most occurring characters

ValueCountFrequency (%)
147544
75.0%
015848
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number63392
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
147544
75.0%
015848
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common63392
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
147544
75.0%
015848
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII63392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
147544
75.0%
015848
 
25.0%

Address
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
1237 S. BEVERLY , TUCSON
15848 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters380352
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1237 S. BEVERLY , TUCSON
2nd row1237 S. BEVERLY , TUCSON
3rd row1237 S. BEVERLY , TUCSON
4th row1237 S. BEVERLY , TUCSON
5th row1237 S. BEVERLY , TUCSON

Common Values

ValueCountFrequency (%)
1237 S. BEVERLY , TUCSON15848
100.0%

Length

2022-10-20T18:50:54.255756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
101115848
100.0%

Most occurring characters

ValueCountFrequency (%)
147544
75.0%
015848
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number63392
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
147544
75.0%
015848
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common63392
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
147544
75.0%
015848
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII63392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
147544
75.0%
015848
 
25.0%

Address
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
1237 S. BEVERLY , TUCSON
15848 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters380352
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1237 S. BEVERLY , TUCSON
2nd row1237 S. BEVERLY , TUCSON
3rd row1237 S. BEVERLY , TUCSON
4th row1237 S. BEVERLY , TUCSON
5th row1237 S. BEVERLY , TUCSON

Common Values

ValueCountFrequency (%)
1237 S. BEVERLY , TUCSON15848
100.0%

Length

2022-10-20T19:29:48.038628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:50:54.387861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:29:48.111700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
123715848
20.0%
s15848
20.0%
beverly15848
20.0%
15848
20.0%
tucson15848
20.0%

Most occurring characters

ValueCountFrequency (%)
63392
16.7%
S31696
 
8.3%
E31696
 
8.3%
115848
 
4.2%
L15848
 
4.2%
O15848
 
4.2%
C15848
 
4.2%
U15848
 
4.2%
T15848
 
4.2%
,15848
 
4.2%
Other values (9)142632
37.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter221872
58.3%
Space Separator63392
 
16.7%
Decimal Number63392
 
16.7%
Other Punctuation31696
 
8.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S31696
14.3%
E31696
14.3%
L15848
7.1%
O15848
7.1%
C15848
7.1%
U15848
7.1%
T15848
7.1%
Y15848
7.1%
V15848
7.1%
R15848
7.1%
Other values (2)31696
14.3%
Decimal Number
ValueCountFrequency (%)
115848
25.0%
215848
25.0%
715848
25.0%
315848
25.0%
Other Punctuation
ValueCountFrequency (%)
,15848
50.0%
.15848
50.0%
Space Separator
ValueCountFrequency (%)
63392
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin221872
58.3%
Common158480
41.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
S31696
14.3%
E31696
14.3%
L15848
7.1%
O15848
7.1%
C15848
7.1%
U15848
7.1%
T15848
7.1%
Y15848
7.1%
V15848
7.1%
R15848
7.1%
Other values (2)31696
14.3%
Common
ValueCountFrequency (%)
63392
40.0%
115848
 
10.0%
,15848
 
10.0%
215848
 
10.0%
.15848
 
10.0%
715848
 
10.0%
315848
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII380352
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
63392
16.7%
S31696
 
8.3%
E31696
 
8.3%
115848
 
4.2%
L15848
 
4.2%
O15848
 
4.2%
C15848
 
4.2%
U15848
 
4.2%
T15848
 
4.2%
,15848
 
4.2%
Other values (9)142632
37.5%

State
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
Arizona
15848 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters110936
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
Arizona15848
100.0%

Length

2022-10-20T18:50:54.502718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
123715848
20.0%
s15848
20.0%
beverly15848
20.0%
15848
20.0%
tucson15848
20.0%

Most occurring characters

ValueCountFrequency (%)
63392
16.7%
S31696
 
8.3%
E31696
 
8.3%
115848
 
4.2%
L15848
 
4.2%
O15848
 
4.2%
C15848
 
4.2%
U15848
 
4.2%
T15848
 
4.2%
,15848
 
4.2%
Other values (9)142632
37.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter221872
58.3%
Space Separator63392
 
16.7%
Decimal Number63392
 
16.7%
Other Punctuation31696
 
8.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S31696
14.3%
E31696
14.3%
L15848
7.1%
O15848
7.1%
C15848
7.1%
U15848
7.1%
T15848
7.1%
Y15848
7.1%
V15848
7.1%
R15848
7.1%
Other values (2)31696
14.3%
Decimal Number
ValueCountFrequency (%)
115848
25.0%
215848
25.0%
715848
25.0%
315848
25.0%
Other Punctuation
ValueCountFrequency (%)
,15848
50.0%
.15848
50.0%
Space Separator
ValueCountFrequency (%)
63392
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin221872
58.3%
Common158480
41.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
S31696
14.3%
E31696
14.3%
L15848
7.1%
O15848
7.1%
C15848
7.1%
U15848
7.1%
T15848
7.1%
Y15848
7.1%
V15848
7.1%
R15848
7.1%
Other values (2)31696
14.3%
Common
ValueCountFrequency (%)
63392
40.0%
115848
 
10.0%
,15848
 
10.0%
215848
 
10.0%
.15848
 
10.0%
715848
 
10.0%
315848
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII380352
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
63392
16.7%
S31696
 
8.3%
E31696
 
8.3%
115848
 
4.2%
L15848
 
4.2%
O15848
 
4.2%
C15848
 
4.2%
U15848
 
4.2%
T15848
 
4.2%
,15848
 
4.2%
Other values (9)142632
37.5%

State
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
Arizona
15848 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters110936
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
Arizona15848
100.0%

Length

2022-10-20T19:29:48.175840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:50:54.642262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:29:48.255943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
arizona15848
100.0%

Most occurring characters

ValueCountFrequency (%)
A15848
14.3%
r15848
14.3%
i15848
14.3%
z15848
14.3%
o15848
14.3%
n15848
14.3%
a15848
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter95088
85.7%
Uppercase Letter15848
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r15848
16.7%
i15848
16.7%
z15848
16.7%
o15848
16.7%
n15848
16.7%
a15848
16.7%
Uppercase Letter
ValueCountFrequency (%)
A15848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin110936
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A15848
14.3%
r15848
14.3%
i15848
14.3%
z15848
14.3%
o15848
14.3%
n15848
14.3%
a15848
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII110936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A15848
14.3%
r15848
14.3%
i15848
14.3%
z15848
14.3%
o15848
14.3%
n15848
14.3%
a15848
14.3%

County
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
Pima
15848 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters63392
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPima
2nd rowPima
3rd rowPima
4th rowPima
5th rowPima

Common Values

ValueCountFrequency (%)
Pima15848
100.0%

Length

2022-10-20T18:50:54.750795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
arizona15848
100.0%

Most occurring characters

ValueCountFrequency (%)
A15848
14.3%
r15848
14.3%
i15848
14.3%
z15848
14.3%
o15848
14.3%
n15848
14.3%
a15848
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter95088
85.7%
Uppercase Letter15848
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r15848
16.7%
i15848
16.7%
z15848
16.7%
o15848
16.7%
n15848
16.7%
a15848
16.7%
Uppercase Letter
ValueCountFrequency (%)
A15848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin110936
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A15848
14.3%
r15848
14.3%
i15848
14.3%
z15848
14.3%
o15848
14.3%
n15848
14.3%
a15848
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII110936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A15848
14.3%
r15848
14.3%
i15848
14.3%
z15848
14.3%
o15848
14.3%
n15848
14.3%
a15848
14.3%

County
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
Pima
15848 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters63392
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPima
2nd rowPima
3rd rowPima
4th rowPima
5th rowPima

Common Values

ValueCountFrequency (%)
Pima15848
100.0%

Length

2022-10-20T19:29:48.318904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:50:54.882958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:29:48.391755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
pima15848
100.0%

Most occurring characters

ValueCountFrequency (%)
P15848
25.0%
i15848
25.0%
m15848
25.0%
a15848
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter47544
75.0%
Uppercase Letter15848
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i15848
33.3%
m15848
33.3%
a15848
33.3%
Uppercase Letter
ValueCountFrequency (%)
P15848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin63392
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P15848
25.0%
i15848
25.0%
m15848
25.0%
a15848
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII63392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P15848
25.0%
i15848
25.0%
m15848
25.0%
a15848
25.0%

City
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
Tucson
15848 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters95088
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTucson
2nd rowTucson
3rd rowTucson
4th rowTucson
5th rowTucson

Common Values

ValueCountFrequency (%)
Tucson15848
100.0%

Length

2022-10-20T18:50:54.993911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
pima15848
100.0%

Most occurring characters

ValueCountFrequency (%)
P15848
25.0%
i15848
25.0%
m15848
25.0%
a15848
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter47544
75.0%
Uppercase Letter15848
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i15848
33.3%
m15848
33.3%
a15848
33.3%
Uppercase Letter
ValueCountFrequency (%)
P15848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin63392
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P15848
25.0%
i15848
25.0%
m15848
25.0%
a15848
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII63392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P15848
25.0%
i15848
25.0%
m15848
25.0%
a15848
25.0%

City
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
Tucson
15848 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters95088
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTucson
2nd rowTucson
3rd rowTucson
4th rowTucson
5th rowTucson

Common Values

ValueCountFrequency (%)
Tucson15848
100.0%

Length

2022-10-20T19:29:48.462328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:50:55.118501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:29:48.534259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
tucson15848
100.0%

Most occurring characters

ValueCountFrequency (%)
T15848
16.7%
u15848
16.7%
c15848
16.7%
s15848
16.7%
o15848
16.7%
n15848
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter79240
83.3%
Uppercase Letter15848
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u15848
20.0%
c15848
20.0%
s15848
20.0%
o15848
20.0%
n15848
20.0%
Uppercase Letter
ValueCountFrequency (%)
T15848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin95088
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T15848
16.7%
u15848
16.7%
c15848
16.7%
s15848
16.7%
o15848
16.7%
n15848
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII95088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T15848
16.7%
u15848
16.7%
c15848
16.7%
s15848
16.7%
o15848
16.7%
n15848
16.7%
Distinct3962
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
Minimum2000-01-01 00:00:00
Maximum2010-12-31 00:00:00
2022-10-20T18:50:55.297304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
tucson15848
100.0%

Most occurring characters

ValueCountFrequency (%)
T15848
16.7%
u15848
16.7%
c15848
16.7%
s15848
16.7%
o15848
16.7%
n15848
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter79240
83.3%
Uppercase Letter15848
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u15848
20.0%
c15848
20.0%
s15848
20.0%
o15848
20.0%
n15848
20.0%
Uppercase Letter
ValueCountFrequency (%)
T15848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin95088
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T15848
16.7%
u15848
16.7%
c15848
16.7%
s15848
16.7%
o15848
16.7%
n15848
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII95088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T15848
16.7%
u15848
16.7%
c15848
16.7%
s15848
16.7%
o15848
16.7%
n15848
16.7%
Distinct3962
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
Minimum2000-01-01 00:00:00
Maximum2010-12-31 00:00:00
2022-10-20T19:29:48.618411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:50:55.506896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:29:48.719331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
Parts per billion
15848 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters269416
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion15848
100.0%

Length

2022-10-20T18:50:55.667818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
Parts per billion
15848 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters269416
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion15848
100.0%

Length

2022-10-20T19:29:48.806206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:50:55.830409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:29:48.879186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts15848
33.3%
per15848
33.3%
billion15848
33.3%

Most occurring characters

ValueCountFrequency (%)
r31696
11.8%
31696
11.8%
i31696
11.8%
l31696
11.8%
P15848
 
5.9%
a15848
 
5.9%
t15848
 
5.9%
s15848
 
5.9%
p15848
 
5.9%
e15848
 
5.9%
Other values (3)47544
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter221872
82.4%
Space Separator31696
 
11.8%
Uppercase Letter15848
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r31696
14.3%
i31696
14.3%
l31696
14.3%
a15848
7.1%
t15848
7.1%
s15848
7.1%
p15848
7.1%
e15848
7.1%
b15848
7.1%
o15848
7.1%
Space Separator
ValueCountFrequency (%)
31696
100.0%
Uppercase Letter
ValueCountFrequency (%)
P15848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin237720
88.2%
Common31696
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r31696
13.3%
i31696
13.3%
l31696
13.3%
P15848
6.7%
a15848
6.7%
t15848
6.7%
s15848
6.7%
p15848
6.7%
e15848
6.7%
b15848
6.7%
Other values (2)31696
13.3%
Common
ValueCountFrequency (%)
31696
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII269416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r31696
11.8%
31696
11.8%
i31696
11.8%
l31696
11.8%
P15848
 
5.9%
a15848
 
5.9%
t15848
 
5.9%
s15848
 
5.9%
p15848
 
5.9%
e15848
 
5.9%
Other values (3)47544
17.6%

NO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1138
Distinct (%)0.07180716809692075
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean14.813391661786977
Minimum1.75
Maximum37.111111
Zeros0
Zeros (%)0.0
Memory size126912
2022-10-20T18:50:55.964796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts15848
33.3%
per15848
33.3%
billion15848
33.3%

Most occurring characters

ValueCountFrequency (%)
r31696
11.8%
31696
11.8%
i31696
11.8%
l31696
11.8%
P15848
 
5.9%
a15848
 
5.9%
t15848
 
5.9%
s15848
 
5.9%
p15848
 
5.9%
e15848
 
5.9%
Other values (3)47544
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter221872
82.4%
Space Separator31696
 
11.8%
Uppercase Letter15848
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r31696
14.3%
i31696
14.3%
l31696
14.3%
a15848
7.1%
t15848
7.1%
s15848
7.1%
p15848
7.1%
e15848
7.1%
b15848
7.1%
o15848
7.1%
Space Separator
ValueCountFrequency (%)
31696
100.0%
Uppercase Letter
ValueCountFrequency (%)
P15848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin237720
88.2%
Common31696
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r31696
13.3%
i31696
13.3%
l31696
13.3%
P15848
6.7%
a15848
6.7%
t15848
6.7%
s15848
6.7%
p15848
6.7%
e15848
6.7%
b15848
6.7%
Other values (2)31696
13.3%
Common
ValueCountFrequency (%)
31696
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII269416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r31696
11.8%
31696
11.8%
i31696
11.8%
l31696
11.8%
P15848
 
5.9%
a15848
 
5.9%
t15848
 
5.9%
s15848
 
5.9%
p15848
 
5.9%
e15848
 
5.9%
Other values (3)47544
17.6%

NO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1138
Distinct (%)0.07180716809692075
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean14.813391661786977
Minimum1.75
Maximum37.111111
Zeros0
Zeros (%)0.0
Memory size126912
2022-10-20T19:29:48.944963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.75
5-th percentile6.333333
Q19.916667
median14.083333
Q318.875
95-th percentile25.958333
Maximum37.111111
Range35.361111
Interquartile range (IQR)8.958333

Descriptive statistics

Standard deviation6.062281238
Coefficient of variation (CV)0.4092432966
Kurtosis-0.3765226938
Mean14.81339166
Median Absolute Deviation (MAD)4.36553
Skewness0.495885267
Sum234762.6311
Variance36.7512538
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.138325482 × 10-9
2022-10-20T18:50:56.148827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.75
5-th percentile6.333333
Q19.916667
median14.083333
Q318.875
95-th percentile25.958333
Maximum37.111111
Range35.361111
Interquartile range (IQR)8.958333

Descriptive statistics

Standard deviation6.062281238
Coefficient of variation (CV)0.4092432966
Kurtosis-0.3765226938
Mean14.81339166
Median Absolute Deviation (MAD)4.36553
Skewness0.495885267
Sum234762.6311
Variance36.7512538
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.138325482 × 10-9
2022-10-20T19:29:49.043209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.95833364
 
0.4%
14.87564
 
0.4%
9.560
 
0.4%
1160
 
0.4%
12.83333356
 
0.4%
12.16666756
 
0.4%
10.37556
 
0.4%
9.87556
 
0.4%
13.45833352
 
0.3%
9.66666752
 
0.3%
Other values (1128)15272
96.4%
ValueCountFrequency (%)
1.754
< 0.1%
2.8333334
< 0.1%
3.3754
< 0.1%
3.3913044
< 0.1%
3.5416674
< 0.1%
3.5454554
< 0.1%
3.5833334
< 0.1%
3.7916674
< 0.1%
3.8754
< 0.1%
3.9166674
< 0.1%
ValueCountFrequency (%)
37.1111114
< 0.1%
364
< 0.1%
35.254
< 0.1%
34.1304354
< 0.1%
34.0416674
< 0.1%
33.254
< 0.1%
33.0416674
< 0.1%
32.758
0.1%
32.5833334
< 0.1%
32.4583334
< 0.1%
2022-10-20T18:50:56.610728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.95833364
 
0.4%
14.87564
 
0.4%
9.560
 
0.4%
1160
 
0.4%
12.83333356
 
0.4%
12.16666756
 
0.4%
10.37556
 
0.4%
9.87556
 
0.4%
13.45833352
 
0.3%
9.66666752
 
0.3%
Other values (1128)15272
96.4%
ValueCountFrequency (%)
1.754
< 0.1%
2.8333334
< 0.1%
3.3754
< 0.1%
3.3913044
< 0.1%
3.5416674
< 0.1%
3.5454554
< 0.1%
3.5833334
< 0.1%
3.7916674
< 0.1%
3.8754
< 0.1%
3.9166674
< 0.1%
ValueCountFrequency (%)
37.1111114
< 0.1%
364
< 0.1%
35.254
< 0.1%
34.1304354
< 0.1%
34.0416674
< 0.1%
33.254
< 0.1%
33.0416674
< 0.1%
32.758
0.1%
32.5833334
< 0.1%
32.4583334
< 0.1%
2022-10-20T19:29:49.214237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct139
Distinct (%)0.008770822816759213
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean32.086067642604746
Minimum5.0
Maximum75.0
Zeros0
Zeros (%)0.0
Memory size126912
2022-10-20T18:50:56.845226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct139
Distinct (%)0.008770822816759213
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean32.086067642604746
Minimum5.0
Maximum75.0
Zeros0
Zeros (%)0.0
Memory size126912
2022-10-20T19:29:49.343428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile14
Q124
median33
Q340
95-th percentile49
Maximum75
Range70
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.72161537
Coefficient of variation (CV)0.3341517412
Kurtosis-0.4269351945
Mean32.08606764
Median Absolute Deviation (MAD)8
Skewness-0.06485587754
Sum508500
Variance114.9530361
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.429366209 × 10-14
2022-10-20T18:50:57.014666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile14
Q124
median33
Q340
95-th percentile49
Maximum75
Range70
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.72161537
Coefficient of variation (CV)0.3341517412
Kurtosis-0.4269351945
Mean32.08606764
Median Absolute Deviation (MAD)8
Skewness-0.06485587754
Sum508500
Variance114.9530361
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.429366209 × 10-14
2022-10-20T19:29:49.442430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38580
 
3.7%
39572
 
3.6%
37572
 
3.6%
35560
 
3.5%
36556
 
3.5%
33536
 
3.4%
41520
 
3.3%
34516
 
3.3%
32492
 
3.1%
29480
 
3.0%
Other values (129)10464
66.0%
ValueCountFrequency (%)
58
 
0.1%
620
 
0.1%
720
 
0.1%
840
 
0.3%
988
0.6%
9.84
 
< 0.1%
10100
0.6%
11116
0.7%
12168
1.1%
13172
1.1%
ValueCountFrequency (%)
758
0.1%
694
 
< 0.1%
664
 
< 0.1%
648
0.1%
63.74
 
< 0.1%
638
0.1%
624
 
< 0.1%
614
 
< 0.1%
6012
0.1%
598
0.1%
2022-10-20T18:50:57.424675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38580
 
3.7%
39572
 
3.6%
37572
 
3.6%
35560
 
3.5%
36556
 
3.5%
33536
 
3.4%
41520
 
3.3%
34516
 
3.3%
32492
 
3.1%
29480
 
3.0%
Other values (129)10464
66.0%
ValueCountFrequency (%)
58
 
0.1%
620
 
0.1%
720
 
0.1%
840
 
0.3%
988
0.6%
9.84
 
< 0.1%
10100
0.6%
11116
0.7%
12168
1.1%
13172
1.1%
ValueCountFrequency (%)
758
0.1%
694
 
< 0.1%
664
 
< 0.1%
648
0.1%
63.74
 
< 0.1%
638
0.1%
624
 
< 0.1%
614
 
< 0.1%
6012
0.1%
598
0.1%
2022-10-20T19:29:49.771544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct24
Distinct (%)0.001514386673397274
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean14.609288238263503
Minimum0
Maximum23
Zeros1628
Zeros (%)0.1027258960121151
Memory size126912
2022-10-20T18:50:57.671185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct24
Distinct (%)0.001514386673397274
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean14.609288238263503
Minimum0
Maximum23
Zeros1628
Zeros (%)0.1027258960121151
Memory size126912
2022-10-20T19:29:49.898708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median19
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.209861418
Coefficient of variation (CV)0.5619617659
Kurtosis-1.209811689
Mean14.60928824
Median Absolute Deviation (MAD)3
Skewness-0.6853668738
Sum231528
Variance67.40182449
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.746269519 × 10-27
2022-10-20T18:50:57.809249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median19
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.209861418
Coefficient of variation (CV)0.5619617659
Kurtosis-1.209811689
Mean14.60928824
Median Absolute Deviation (MAD)3
Skewness-0.6853668738
Sum231528
Variance67.40182449
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.746269519 × 10-27
2022-10-20T19:29:49.993543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
212160
13.6%
221996
12.6%
201776
11.2%
01628
10.3%
61484
9.4%
191416
8.9%
231212
7.6%
181184
7.5%
7760
 
4.8%
5500
 
3.2%
Other values (14)1732
10.9%
ValueCountFrequency (%)
01628
10.3%
1376
 
2.4%
2224
 
1.4%
396
 
0.6%
488
 
0.6%
5500
 
3.2%
61484
9.4%
7760
4.8%
8228
 
1.4%
9100
 
0.6%
ValueCountFrequency (%)
231212
7.6%
221996
12.6%
212160
13.6%
201776
11.2%
191416
8.9%
181184
7.5%
17412
 
2.6%
1656
 
0.4%
1544
 
0.3%
148
 
0.1%
2022-10-20T18:50:58.195261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
212160
13.6%
221996
12.6%
201776
11.2%
01628
10.3%
61484
9.4%
191416
8.9%
231212
7.6%
181184
7.5%
7760
 
4.8%
5500
 
3.2%
Other values (14)1732
10.9%
ValueCountFrequency (%)
01628
10.3%
1376
 
2.4%
2224
 
1.4%
396
 
0.6%
488
 
0.6%
5500
 
3.2%
61484
9.4%
7760
4.8%
8228
 
1.4%
9100
 
0.6%
ValueCountFrequency (%)
231212
7.6%
221996
12.6%
212160
13.6%
201776
11.2%
191416
8.9%
181184
7.5%
17412
 
2.6%
1656
 
0.4%
1544
 
0.3%
148
 
0.1%
2022-10-20T19:29:50.272680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct60
Distinct (%)0.003785966683493185
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean30.273851590106005
Minimum5
Maximum73
Zeros0
Zeros (%)0.0
Memory size126912
2022-10-20T18:50:58.425479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct60
Distinct (%)0.003785966683493185
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean30.273851590106005
Minimum5
Maximum73
Zeros0
Zeros (%)0.0
Memory size126912
2022-10-20T19:29:50.403083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile13
Q123
median31
Q338
95-th percentile46
Maximum73
Range68
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.15632058
Coefficient of variation (CV)0.3354816136
Kurtosis-0.3679722018
Mean30.27385159
Median Absolute Deviation (MAD)7
Skewness-0.06196075259
Sum479780
Variance103.1508477
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.077983109 × 10-14
2022-10-20T18:50:58.758984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile13
Q123
median31
Q338
95-th percentile46
Maximum73
Range68
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.15632058
Coefficient of variation (CV)0.3354816136
Kurtosis-0.3679722018
Mean30.27385159
Median Absolute Deviation (MAD)7
Skewness-0.06196075259
Sum479780
Variance103.1508477
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.077983109 × 10-14
2022-10-20T19:29:50.649422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25768
 
4.8%
42756
 
4.8%
36608
 
3.8%
37588
 
3.7%
35588
 
3.7%
33576
 
3.6%
31564
 
3.6%
34564
 
3.6%
32532
 
3.4%
39520
 
3.3%
Other values (50)9784
61.7%
ValueCountFrequency (%)
58
 
0.1%
620
 
0.1%
720
 
0.1%
8132
0.8%
9100
0.6%
10116
0.7%
11168
1.1%
12176
1.1%
13204
1.3%
14232
1.5%
ValueCountFrequency (%)
738
 
0.1%
674
 
< 0.1%
644
 
< 0.1%
628
 
0.1%
6112
0.1%
604
 
< 0.1%
584
 
< 0.1%
5712
0.1%
568
 
0.1%
5520
0.1%
2022-10-20T18:50:59.433927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25768
 
4.8%
42756
 
4.8%
36608
 
3.8%
37588
 
3.7%
35588
 
3.7%
33576
 
3.6%
31564
 
3.6%
34564
 
3.6%
32532
 
3.4%
39520
 
3.3%
Other values (50)9784
61.7%
ValueCountFrequency (%)
58
 
0.1%
620
 
0.1%
720
 
0.1%
8132
0.8%
9100
0.6%
10116
0.7%
11168
1.1%
12176
1.1%
13204
1.3%
14232
1.5%
ValueCountFrequency (%)
738
 
0.1%
674
 
< 0.1%
644
 
< 0.1%
628
 
0.1%
6112
0.1%
604
 
< 0.1%
584
 
< 0.1%
5712
0.1%
568
 
0.1%
5520
0.1%
2022-10-20T19:29:50.931250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
Parts per million
15848 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters269416
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million15848
100.0%

Length

2022-10-20T18:50:59.684093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
Parts per million
15848 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters269416
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million15848
100.0%

Length

2022-10-20T19:29:51.067878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:50:59.812917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:29:51.148632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts15848
33.3%
per15848
33.3%
million15848
33.3%

Most occurring characters

ValueCountFrequency (%)
r31696
11.8%
31696
11.8%
i31696
11.8%
l31696
11.8%
P15848
 
5.9%
a15848
 
5.9%
t15848
 
5.9%
s15848
 
5.9%
p15848
 
5.9%
e15848
 
5.9%
Other values (3)47544
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter221872
82.4%
Space Separator31696
 
11.8%
Uppercase Letter15848
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r31696
14.3%
i31696
14.3%
l31696
14.3%
a15848
7.1%
t15848
7.1%
s15848
7.1%
p15848
7.1%
e15848
7.1%
m15848
7.1%
o15848
7.1%
Space Separator
ValueCountFrequency (%)
31696
100.0%
Uppercase Letter
ValueCountFrequency (%)
P15848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin237720
88.2%
Common31696
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r31696
13.3%
i31696
13.3%
l31696
13.3%
P15848
6.7%
a15848
6.7%
t15848
6.7%
s15848
6.7%
p15848
6.7%
e15848
6.7%
m15848
6.7%
Other values (2)31696
13.3%
Common
ValueCountFrequency (%)
31696
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII269416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r31696
11.8%
31696
11.8%
i31696
11.8%
l31696
11.8%
P15848
 
5.9%
a15848
 
5.9%
t15848
 
5.9%
s15848
 
5.9%
p15848
 
5.9%
e15848
 
5.9%
Other values (3)47544
17.6%

O3 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1030
Distinct (%)0.06499242806663301
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.027467133266027257
Minimum0.004458
Maximum0.058167
Zeros0
Zeros (%)0.0
Memory size126912
2022-10-20T18:50:59.929733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts15848
33.3%
per15848
33.3%
million15848
33.3%

Most occurring characters

ValueCountFrequency (%)
r31696
11.8%
31696
11.8%
i31696
11.8%
l31696
11.8%
P15848
 
5.9%
a15848
 
5.9%
t15848
 
5.9%
s15848
 
5.9%
p15848
 
5.9%
e15848
 
5.9%
Other values (3)47544
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter221872
82.4%
Space Separator31696
 
11.8%
Uppercase Letter15848
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r31696
14.3%
i31696
14.3%
l31696
14.3%
a15848
7.1%
t15848
7.1%
s15848
7.1%
p15848
7.1%
e15848
7.1%
m15848
7.1%
o15848
7.1%
Space Separator
ValueCountFrequency (%)
31696
100.0%
Uppercase Letter
ValueCountFrequency (%)
P15848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin237720
88.2%
Common31696
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r31696
13.3%
i31696
13.3%
l31696
13.3%
P15848
6.7%
a15848
6.7%
t15848
6.7%
s15848
6.7%
p15848
6.7%
e15848
6.7%
m15848
6.7%
Other values (2)31696
13.3%
Common
ValueCountFrequency (%)
31696
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII269416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r31696
11.8%
31696
11.8%
i31696
11.8%
l31696
11.8%
P15848
 
5.9%
a15848
 
5.9%
t15848
 
5.9%
s15848
 
5.9%
p15848
 
5.9%
e15848
 
5.9%
Other values (3)47544
17.6%

O3 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1030
Distinct (%)0.06499242806663301
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.027467133266027257
Minimum0.004458
Maximum0.058167
Zeros0
Zeros (%)0.0
Memory size126912
2022-10-20T19:29:51.209294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.004458
5-th percentile0.011
Q10.019583
median0.027521
Q30.034917
95-th percentile0.043833
Maximum0.058167
Range0.053709
Interquartile range (IQR)0.015334

Descriptive statistics

Standard deviation0.01018572271
Coefficient of variation (CV)0.3708331196
Kurtosis-0.6399953057
Mean0.02746713327
Median Absolute Deviation (MAD)0.007688
Skewness0.05762996938
Sum435.299128
Variance0.0001037489472
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.279270525 × 10-7
2022-10-20T18:51:00.127310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.004458
5-th percentile0.011
Q10.019583
median0.027521
Q30.034917
95-th percentile0.043833
Maximum0.058167
Range0.053709
Interquartile range (IQR)0.015334

Descriptive statistics

Standard deviation0.01018572271
Coefficient of variation (CV)0.3708331196
Kurtosis-0.6399953057
Mean0.02746713327
Median Absolute Deviation (MAD)0.007688
Skewness0.05762996938
Sum435.299128
Variance0.0001037489472
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.279270525 × 10-7
2022-10-20T19:29:51.300546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03037560
 
0.4%
0.02304248
 
0.3%
0.02891744
 
0.3%
0.02620844
 
0.3%
0.03108344
 
0.3%
0.03183344
 
0.3%
0.0342544
 
0.3%
0.03791744
 
0.3%
0.02741744
 
0.3%
0.020540
 
0.3%
Other values (1020)15392
97.1%
ValueCountFrequency (%)
0.0044584
< 0.1%
0.0046254
< 0.1%
0.0047084
< 0.1%
0.004754
< 0.1%
0.0049174
< 0.1%
0.0054
< 0.1%
0.0051254
< 0.1%
0.005254
< 0.1%
0.0052924
< 0.1%
0.0053084
< 0.1%
ValueCountFrequency (%)
0.0581674
< 0.1%
0.0571674
< 0.1%
0.0562924
< 0.1%
0.0560424
< 0.1%
0.0558334
< 0.1%
0.0552924
< 0.1%
0.0549174
< 0.1%
0.0548754
< 0.1%
0.0547088
0.1%
0.05454
< 0.1%
2022-10-20T18:51:00.523827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03037560
 
0.4%
0.02304248
 
0.3%
0.02891744
 
0.3%
0.02620844
 
0.3%
0.03108344
 
0.3%
0.03183344
 
0.3%
0.0342544
 
0.3%
0.03791744
 
0.3%
0.02741744
 
0.3%
0.020540
 
0.3%
Other values (1020)15392
97.1%
ValueCountFrequency (%)
0.0044584
< 0.1%
0.0046254
< 0.1%
0.0047084
< 0.1%
0.004754
< 0.1%
0.0049174
< 0.1%
0.0054
< 0.1%
0.0051254
< 0.1%
0.005254
< 0.1%
0.0052924
< 0.1%
0.0053084
< 0.1%
ValueCountFrequency (%)
0.0581674
< 0.1%
0.0571674
< 0.1%
0.0562924
< 0.1%
0.0560424
< 0.1%
0.0558334
< 0.1%
0.0552924
< 0.1%
0.0549174
< 0.1%
0.0548754
< 0.1%
0.0547088
0.1%
0.05454
< 0.1%
2022-10-20T19:29:51.647293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct75
Distinct (%)0.004732458354366482
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.04384830893488137
Minimum0.007
Maximum0.085
Zeros0
Zeros (%)0.0
Memory size126912
2022-10-20T18:51:00.761440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct75
Distinct (%)0.004732458354366482
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.04384830893488137
Minimum0.007
Maximum0.085
Zeros0
Zeros (%)0.0
Memory size126912
2022-10-20T19:29:51.812014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.007
5-th percentile0.024
Q10.035
median0.044
Q30.053
95-th percentile0.064
Maximum0.085
Range0.078
Interquartile range (IQR)0.018

Descriptive statistics

Standard deviation0.01232026543
Coefficient of variation (CV)0.2809746995
Kurtosis-0.30039261
Mean0.04384830893
Median Absolute Deviation (MAD)0.009
Skewness-0.01111671127
Sum694.908
Variance0.0001517889402
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value9.120626494 × 10-8
2022-10-20T18:51:00.929131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.007
5-th percentile0.024
Q10.035
median0.044
Q30.053
95-th percentile0.064
Maximum0.085
Range0.078
Interquartile range (IQR)0.018

Descriptive statistics

Standard deviation0.01232026543
Coefficient of variation (CV)0.2809746995
Kurtosis-0.30039261
Mean0.04384830893
Median Absolute Deviation (MAD)0.009
Skewness-0.01111671127
Sum694.908
Variance0.0001517889402
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value9.120626494 × 10-8
2022-10-20T19:29:51.909472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.037520
 
3.3%
0.049492
 
3.1%
0.039488
 
3.1%
0.046484
 
3.1%
0.053472
 
3.0%
0.045468
 
3.0%
0.052468
 
3.0%
0.04468
 
3.0%
0.042464
 
2.9%
0.038460
 
2.9%
Other values (65)11064
69.8%
ValueCountFrequency (%)
0.0074
 
< 0.1%
0.0084
 
< 0.1%
0.00912
 
0.1%
0.0112
 
0.1%
0.0114
 
< 0.1%
0.01216
 
0.1%
0.01332
0.2%
0.01428
0.2%
0.01544
0.3%
0.01660
0.4%
ValueCountFrequency (%)
0.0854
 
< 0.1%
0.0834
 
< 0.1%
0.084
 
< 0.1%
0.0798
 
0.1%
0.07812
 
0.1%
0.0764
 
< 0.1%
0.07544
0.3%
0.07428
0.2%
0.07332
0.2%
0.07220
0.1%
2022-10-20T18:51:01.346937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.037520
 
3.3%
0.049492
 
3.1%
0.039488
 
3.1%
0.046484
 
3.1%
0.053472
 
3.0%
0.045468
 
3.0%
0.052468
 
3.0%
0.04468
 
3.0%
0.042464
 
2.9%
0.038460
 
2.9%
Other values (65)11064
69.8%
ValueCountFrequency (%)
0.0074
 
< 0.1%
0.0084
 
< 0.1%
0.00912
 
0.1%
0.0112
 
0.1%
0.0114
 
< 0.1%
0.01216
 
0.1%
0.01332
0.2%
0.01428
0.2%
0.01544
0.3%
0.01660
0.4%
ValueCountFrequency (%)
0.0854
 
< 0.1%
0.0834
 
< 0.1%
0.084
 
< 0.1%
0.0798
 
0.1%
0.07812
 
0.1%
0.0764
 
< 0.1%
0.07544
0.3%
0.07428
0.2%
0.07332
0.2%
0.07220
0.1%
2022-10-20T19:29:52.250518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct23
Distinct (%)0.0014512872286723878
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean9.780918727915195
Minimum0
Maximum23
Zeros128
Zeros (%)0.008076728924785462
Memory size126912
2022-10-20T18:51:01.582438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct23
Distinct (%)0.0014512872286723878
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean9.780918727915195
Minimum0
Maximum23
Zeros128
Zeros (%)0.008076728924785462
Memory size126912
2022-10-20T19:29:52.455737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q19
median10
Q310
95-th percentile11
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.910196719
Coefficient of variation (CV)0.1952982918
Kurtosis21.25303043
Mean9.780918728
Median Absolute Deviation (MAD)1
Skewness1.612845242
Sum155008
Variance3.648851506
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.963315146 × 10-29
2022-10-20T18:51:01.727564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q19
median10
Q310
95-th percentile11
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.910196719
Coefficient of variation (CV)0.1952982918
Kurtosis21.25303043
Mean9.780918728
Median Absolute Deviation (MAD)1
Skewness1.612845242
Sum155008
Variance3.648851506
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.963315146 × 10-29
2022-10-20T19:29:52.534507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
106416
40.5%
95152
32.5%
112324
 
14.7%
8944
 
6.0%
12276
 
1.7%
7152
 
1.0%
0128
 
0.8%
1372
 
0.5%
2260
 
0.4%
1540
 
0.3%
Other values (13)284
 
1.8%
ValueCountFrequency (%)
0128
 
0.8%
116
 
0.1%
28
 
0.1%
320
 
0.1%
512
 
0.1%
628
 
0.2%
7152
 
1.0%
8944
 
6.0%
95152
32.5%
106416
40.5%
ValueCountFrequency (%)
2336
0.2%
2260
0.4%
2136
0.2%
2036
0.2%
1932
0.2%
184
 
< 0.1%
174
 
< 0.1%
1616
 
0.1%
1540
0.3%
1436
0.2%
2022-10-20T18:51:02.142269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
106416
40.5%
95152
32.5%
112324
 
14.7%
8944
 
6.0%
12276
 
1.7%
7152
 
1.0%
0128
 
0.8%
1372
 
0.5%
2260
 
0.4%
1540
 
0.3%
Other values (13)284
 
1.8%
ValueCountFrequency (%)
0128
 
0.8%
116
 
0.1%
28
 
0.1%
320
 
0.1%
512
 
0.1%
628
 
0.2%
7152
 
1.0%
8944
 
6.0%
95152
32.5%
106416
40.5%
ValueCountFrequency (%)
2336
0.2%
2260
0.4%
2136
0.2%
2036
0.2%
1932
0.2%
184
 
< 0.1%
174
 
< 0.1%
1616
 
0.1%
1540
0.3%
1436
0.2%
2022-10-20T19:29:52.704284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct67
Distinct (%)0.00422766279656739
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean38.393235739525494
Minimum6
Maximum124
Zeros0
Zeros (%)0.0
Memory size126912
2022-10-20T18:51:02.390105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct67
Distinct (%)0.00422766279656739
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean38.393235739525494
Minimum6
Maximum124
Zeros0
Zeros (%)0.0
Memory size126912
2022-10-20T19:29:52.832108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile20
Q130
median37
Q345
95-th percentile64
Maximum124
Range118
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.63542225
Coefficient of variation (CV)0.3551516821
Kurtosis4.351042699
Mean38.39323574
Median Absolute Deviation (MAD)7
Skewness1.430332657
Sum608456
Variance185.92474
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.51990373 × 10-11
2022-10-20T18:51:02.589131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile20
Q130
median37
Q345
95-th percentile64
Maximum124
Range118
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.63542225
Coefficient of variation (CV)0.3551516821
Kurtosis4.351042699
Mean38.39323574
Median Absolute Deviation (MAD)7
Skewness1.430332657
Sum608456
Variance185.92474
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.51990373 × 10-11
2022-10-20T19:29:52.933005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31944
 
6.0%
42904
 
5.7%
36872
 
5.5%
47748
 
4.7%
25532
 
3.4%
33488
 
3.1%
39484
 
3.1%
45472
 
3.0%
34468
 
3.0%
44468
 
3.0%
Other values (57)9468
59.7%
ValueCountFrequency (%)
64
 
< 0.1%
74
 
< 0.1%
824
 
0.2%
94
 
< 0.1%
1016
 
0.1%
1132
 
0.2%
1228
 
0.2%
1344
0.3%
1492
0.6%
1540
0.3%
ValueCountFrequency (%)
1244
 
< 0.1%
1194
 
< 0.1%
1114
 
< 0.1%
1098
 
0.1%
10612
 
0.1%
1014
 
< 0.1%
10044
0.3%
9728
0.2%
9332
0.2%
9020
0.1%
2022-10-20T18:51:03.201550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31944
 
6.0%
42904
 
5.7%
36872
 
5.5%
47748
 
4.7%
25532
 
3.4%
33488
 
3.1%
39484
 
3.1%
45472
 
3.0%
34468
 
3.0%
44468
 
3.0%
Other values (57)9468
59.7%
ValueCountFrequency (%)
64
 
< 0.1%
74
 
< 0.1%
824
 
0.2%
94
 
< 0.1%
1016
 
0.1%
1132
 
0.2%
1228
 
0.2%
1344
0.3%
1492
0.6%
1540
0.3%
ValueCountFrequency (%)
1244
 
< 0.1%
1194
 
< 0.1%
1114
 
< 0.1%
1098
 
0.1%
10612
 
0.1%
1014
 
< 0.1%
10044
0.3%
9728
0.2%
9332
0.2%
9020
0.1%
2022-10-20T19:29:53.142036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
Parts per billion
15848 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters269416
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion15848
100.0%

Length

2022-10-20T18:51:03.443332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
Parts per billion
15848 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters269416
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion15848
100.0%

Length

2022-10-20T19:29:53.275344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:51:03.596187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:29:53.361618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts15848
33.3%
per15848
33.3%
billion15848
33.3%

Most occurring characters

ValueCountFrequency (%)
r31696
11.8%
31696
11.8%
i31696
11.8%
l31696
11.8%
P15848
 
5.9%
a15848
 
5.9%
t15848
 
5.9%
s15848
 
5.9%
p15848
 
5.9%
e15848
 
5.9%
Other values (3)47544
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter221872
82.4%
Space Separator31696
 
11.8%
Uppercase Letter15848
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r31696
14.3%
i31696
14.3%
l31696
14.3%
a15848
7.1%
t15848
7.1%
s15848
7.1%
p15848
7.1%
e15848
7.1%
b15848
7.1%
o15848
7.1%
Space Separator
ValueCountFrequency (%)
31696
100.0%
Uppercase Letter
ValueCountFrequency (%)
P15848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin237720
88.2%
Common31696
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r31696
13.3%
i31696
13.3%
l31696
13.3%
P15848
6.7%
a15848
6.7%
t15848
6.7%
s15848
6.7%
p15848
6.7%
e15848
6.7%
b15848
6.7%
Other values (2)31696
13.3%
Common
ValueCountFrequency (%)
31696
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII269416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r31696
11.8%
31696
11.8%
i31696
11.8%
l31696
11.8%
P15848
 
5.9%
a15848
 
5.9%
t15848
 
5.9%
s15848
 
5.9%
p15848
 
5.9%
e15848
 
5.9%
Other values (3)47544
17.6%

SO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct690
Distinct (%)0.04353861686017163
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.9539306227915194
Minimum0.0
Maximum7.625
Zeros678
Zeros (%)0.04278142352347299
Memory size126912
2022-10-20T18:51:03.708003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts15848
33.3%
per15848
33.3%
billion15848
33.3%

Most occurring characters

ValueCountFrequency (%)
r31696
11.8%
31696
11.8%
i31696
11.8%
l31696
11.8%
P15848
 
5.9%
a15848
 
5.9%
t15848
 
5.9%
s15848
 
5.9%
p15848
 
5.9%
e15848
 
5.9%
Other values (3)47544
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter221872
82.4%
Space Separator31696
 
11.8%
Uppercase Letter15848
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r31696
14.3%
i31696
14.3%
l31696
14.3%
a15848
7.1%
t15848
7.1%
s15848
7.1%
p15848
7.1%
e15848
7.1%
b15848
7.1%
o15848
7.1%
Space Separator
ValueCountFrequency (%)
31696
100.0%
Uppercase Letter
ValueCountFrequency (%)
P15848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin237720
88.2%
Common31696
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r31696
13.3%
i31696
13.3%
l31696
13.3%
P15848
6.7%
a15848
6.7%
t15848
6.7%
s15848
6.7%
p15848
6.7%
e15848
6.7%
b15848
6.7%
Other values (2)31696
13.3%
Common
ValueCountFrequency (%)
31696
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII269416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r31696
11.8%
31696
11.8%
i31696
11.8%
l31696
11.8%
P15848
 
5.9%
a15848
 
5.9%
t15848
 
5.9%
s15848
 
5.9%
p15848
 
5.9%
e15848
 
5.9%
Other values (3)47544
17.6%

SO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct690
Distinct (%)0.04353861686017163
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.9539306227915194
Minimum0.0
Maximum7.625
Zeros678
Zeros (%)0.04278142352347299
Memory size126912
2022-10-20T19:29:53.424155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0375
Q10.4375
median0.875
Q31.1875
95-th percentile2.385714
Maximum7.625
Range7.625
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.771519512
Coefficient of variation (CV)0.8087794789
Kurtosis6.819665436
Mean0.9539306228
Median Absolute Deviation (MAD)0.375
Skewness1.985839516
Sum15117.89251
Variance0.5952423574
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.1593961 × 10-11
2022-10-20T18:51:03.879244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0375
Q10.4375
median0.875
Q31.1875
95-th percentile2.385714
Maximum7.625
Range7.625
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.771519512
Coefficient of variation (CV)0.8087794789
Kurtosis6.819665436
Mean0.9539306228
Median Absolute Deviation (MAD)0.375
Skewness1.985839516
Sum15117.89251
Variance0.5952423574
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.1593961 × 10-11
2022-10-20T19:29:53.521318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1858
 
5.4%
0678
 
4.3%
0.958333228
 
1.4%
1.125222
 
1.4%
1.083333222
 
1.4%
1.041667222
 
1.4%
0.75204
 
1.3%
0.916667192
 
1.2%
0.875182
 
1.1%
0.5176
 
1.1%
Other values (680)12664
79.9%
ValueCountFrequency (%)
0678
4.3%
0.0041672
 
< 0.1%
0.0083332
 
< 0.1%
0.01254
 
< 0.1%
0.0166672
 
< 0.1%
0.0208334
 
< 0.1%
0.0227272
 
< 0.1%
0.0256
 
< 0.1%
0.0291672
 
< 0.1%
0.0375142
 
0.9%
ValueCountFrequency (%)
7.6252
< 0.1%
7.61252
< 0.1%
6.5833332
< 0.1%
6.5752
< 0.1%
5.6252
< 0.1%
5.5833332
< 0.1%
5.5752
< 0.1%
5.552
< 0.1%
5.54
< 0.1%
5.4752
< 0.1%
2022-10-20T18:51:04.331360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1858
 
5.4%
0678
 
4.3%
0.958333228
 
1.4%
1.125222
 
1.4%
1.083333222
 
1.4%
1.041667222
 
1.4%
0.75204
 
1.3%
0.916667192
 
1.2%
0.875182
 
1.1%
0.5176
 
1.1%
Other values (680)12664
79.9%
ValueCountFrequency (%)
0678
4.3%
0.0041672
 
< 0.1%
0.0083332
 
< 0.1%
0.01254
 
< 0.1%
0.0166672
 
< 0.1%
0.0208334
 
< 0.1%
0.0227272
 
< 0.1%
0.0256
 
< 0.1%
0.0291672
 
< 0.1%
0.0375142
 
0.9%
ValueCountFrequency (%)
7.6252
< 0.1%
7.61252
< 0.1%
6.5833332
< 0.1%
6.5752
< 0.1%
5.6252
< 0.1%
5.5833332
< 0.1%
5.5752
< 0.1%
5.552
< 0.1%
5.54
< 0.1%
5.4752
< 0.1%
2022-10-20T19:29:53.792447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct68
Distinct (%)0.004290762241292277
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean2.3709111559818274
Minimum0.0
Maximum27.0
Zeros678
Zeros (%)0.04278142352347299
Memory size126912
2022-10-20T18:51:04.576385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct68
Distinct (%)0.004290762241292277
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean2.3709111559818274
Minimum0.0
Maximum27.0
Zeros678
Zeros (%)0.04278142352347299
Memory size126912
2022-10-20T19:29:53.926225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11
median2
Q33
95-th percentile6.6
Maximum27
Range27
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.212725149
Coefficient of variation (CV)0.9332805001
Kurtosis13.04247454
Mean2.370911156
Median Absolute Deviation (MAD)1
Skewness2.762294996
Sum37574.2
Variance4.896152587
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.972347918 × 10-17
2022-10-20T18:51:04.746366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11
median2
Q33
95-th percentile6.6
Maximum27
Range27
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.212725149
Coefficient of variation (CV)0.9332805001
Kurtosis13.04247454
Mean2.370911156
Median Absolute Deviation (MAD)1
Skewness2.762294996
Sum37574.2
Variance4.896152587
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.972347918 × 10-17
2022-10-20T19:29:54.019911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14530
28.6%
22420
15.3%
31358
 
8.6%
4906
 
5.7%
1.3900
 
5.7%
1.6794
 
5.0%
0678
 
4.3%
5526
 
3.3%
2.3432
 
2.7%
0.6426
 
2.7%
Other values (58)2878
18.2%
ValueCountFrequency (%)
0678
4.3%
0.126
 
0.2%
0.224
 
0.2%
0.3308
1.9%
0.440
 
0.3%
0.516
 
0.1%
0.6426
2.7%
0.728
 
0.2%
0.824
 
0.2%
0.928
 
0.2%
ValueCountFrequency (%)
272
 
< 0.1%
242
 
< 0.1%
232
 
< 0.1%
222
 
< 0.1%
212
 
< 0.1%
202
 
< 0.1%
19.32
 
< 0.1%
194
< 0.1%
186
< 0.1%
178
0.1%
2022-10-20T18:51:05.107764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14530
28.6%
22420
15.3%
31358
 
8.6%
4906
 
5.7%
1.3900
 
5.7%
1.6794
 
5.0%
0678
 
4.3%
5526
 
3.3%
2.3432
 
2.7%
0.6426
 
2.7%
Other values (58)2878
18.2%
ValueCountFrequency (%)
0678
4.3%
0.126
 
0.2%
0.224
 
0.2%
0.3308
1.9%
0.440
 
0.3%
0.516
 
0.1%
0.6426
2.7%
0.728
 
0.2%
0.824
 
0.2%
0.928
 
0.2%
ValueCountFrequency (%)
272
 
< 0.1%
242
 
< 0.1%
232
 
< 0.1%
222
 
< 0.1%
212
 
< 0.1%
202
 
< 0.1%
19.32
 
< 0.1%
194
< 0.1%
186
< 0.1%
178
0.1%
2022-10-20T19:29:54.189912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct24
Distinct (%)0.001514386673397274
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean9.943589096415952
Minimum0
Maximum23
Zeros1930
Zeros (%)0.1217819283190308
Memory size126912
2022-10-20T18:51:05.341022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct24
Distinct (%)0.001514386673397274
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean9.943589096415952
Minimum0
Maximum23
Zeros1930
Zeros (%)0.1217819283190308
Memory size126912
2022-10-20T19:29:54.327300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q314
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.858151204
Coefficient of variation (CV)0.6897058132
Kurtosis-0.8534940752
Mean9.943589096
Median Absolute Deviation (MAD)4
Skewness0.2294311034
Sum157586
Variance47.03423794
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.263201089 × 10-25
2022-10-20T18:51:05.670370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q314
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.858151204
Coefficient of variation (CV)0.6897058132
Kurtosis-0.8534940752
Mean9.943589096
Median Absolute Deviation (MAD)4
Skewness0.2294311034
Sum157586
Variance47.03423794
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.263201089 × 10-25
2022-10-20T19:29:54.583223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
112354
14.9%
01930
12.2%
81852
11.7%
21780
11.2%
141170
 
7.4%
201020
 
6.4%
23894
 
5.6%
9568
 
3.6%
10564
 
3.6%
17532
 
3.4%
Other values (14)3184
20.1%
ValueCountFrequency (%)
01930
12.2%
1186
 
1.2%
21780
11.2%
376
 
0.5%
452
 
0.3%
5276
 
1.7%
6328
 
2.1%
7494
 
3.1%
81852
11.7%
9568
 
3.6%
ValueCountFrequency (%)
23894
5.6%
22172
 
1.1%
21222
 
1.4%
201020
6.4%
19214
 
1.4%
18202
 
1.3%
17532
3.4%
16108
 
0.7%
15116
 
0.7%
141170
7.4%
2022-10-20T18:51:06.080518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
112354
14.9%
01930
12.2%
81852
11.7%
21780
11.2%
141170
 
7.4%
201020
 
6.4%
23894
 
5.6%
9568
 
3.6%
10564
 
3.6%
17532
 
3.4%
Other values (14)3184
20.1%
ValueCountFrequency (%)
01930
12.2%
1186
 
1.2%
21780
11.2%
376
 
0.5%
452
 
0.3%
5276
 
1.7%
6328
 
2.1%
7494
 
3.1%
81852
11.7%
9568
 
3.6%
ValueCountFrequency (%)
23894
5.6%
22172
 
1.1%
21222
 
1.4%
201020
6.4%
19214
 
1.4%
18202
 
1.3%
17532
3.4%
16108
 
0.7%
15116
 
0.7%
141170
7.4%
2022-10-20T19:29:54.790413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 AQI
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct26
Distinct (%)0.3%
Missing7924
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean3.956587582
Minimum0
Maximum39
Zeros456
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size123.9 KiB
2022-10-20T18:51:06.335353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 AQI
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct26
Distinct (%)0.3%
Missing7924
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean3.956587582
Minimum0
Maximum39
Zeros456
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size123.9 KiB
2022-10-20T19:29:54.923028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile11
Maximum39
Range39
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.859272976
Coefficient of variation (CV)0.9754044099
Kurtosis8.792113086
Mean3.956587582
Median Absolute Deviation (MAD)2
Skewness2.277162305
Sum31352
Variance14.8939879
MonotonicityNot monotonic
2022-10-20T18:51:06.508466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile11
Maximum39
Range39
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.859272976
Coefficient of variation (CV)0.9754044099
Kurtosis8.792113086
Mean3.956587582
Median Absolute Deviation (MAD)2
Skewness2.277162305
Sum31352
Variance14.8939879
MonotonicityNot monotonic
2022-10-20T19:29:55.005733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
12486
 
15.7%
31734
 
10.9%
41060
 
6.7%
6740
 
4.7%
7462
 
2.9%
0456
 
2.9%
9322
 
2.0%
10190
 
1.2%
11172
 
1.1%
1392
 
0.6%
Other values (16)210
 
1.3%
(Missing)7924
50.0%
ValueCountFrequency (%)
0456
 
2.9%
12486
15.7%
31734
10.9%
41060
6.7%
6740
 
4.7%
7462
 
2.9%
9322
 
2.0%
10190
 
1.2%
11172
 
1.1%
1392
 
0.6%
ValueCountFrequency (%)
392
 
< 0.1%
342
 
< 0.1%
332
 
< 0.1%
312
 
< 0.1%
302
 
< 0.1%
292
 
< 0.1%
274
< 0.1%
266
< 0.1%
248
0.1%
238
0.1%

CO Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
Parts per million
15848 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters269416
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million15848
100.0%

Length

2022-10-20T18:51:06.655064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
12486
 
15.7%
31734
 
10.9%
41060
 
6.7%
6740
 
4.7%
7462
 
2.9%
0456
 
2.9%
9322
 
2.0%
10190
 
1.2%
11172
 
1.1%
1392
 
0.6%
Other values (16)210
 
1.3%
(Missing)7924
50.0%
ValueCountFrequency (%)
0456
 
2.9%
12486
15.7%
31734
10.9%
41060
6.7%
6740
 
4.7%
7462
 
2.9%
9322
 
2.0%
10190
 
1.2%
11172
 
1.1%
1392
 
0.6%
ValueCountFrequency (%)
392
 
< 0.1%
342
 
< 0.1%
332
 
< 0.1%
312
 
< 0.1%
302
 
< 0.1%
292
 
< 0.1%
274
< 0.1%
266
< 0.1%
248
0.1%
238
0.1%

CO Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
Parts per million
15848 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters269416
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million15848
100.0%

Length

2022-10-20T19:29:55.083753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:51:06.786000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:29:55.157853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts15848
33.3%
per15848
33.3%
million15848
33.3%

Most occurring characters

ValueCountFrequency (%)
r31696
11.8%
31696
11.8%
i31696
11.8%
l31696
11.8%
P15848
 
5.9%
a15848
 
5.9%
t15848
 
5.9%
s15848
 
5.9%
p15848
 
5.9%
e15848
 
5.9%
Other values (3)47544
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter221872
82.4%
Space Separator31696
 
11.8%
Uppercase Letter15848
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r31696
14.3%
i31696
14.3%
l31696
14.3%
a15848
7.1%
t15848
7.1%
s15848
7.1%
p15848
7.1%
e15848
7.1%
m15848
7.1%
o15848
7.1%
Space Separator
ValueCountFrequency (%)
31696
100.0%
Uppercase Letter
ValueCountFrequency (%)
P15848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin237720
88.2%
Common31696
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r31696
13.3%
i31696
13.3%
l31696
13.3%
P15848
6.7%
a15848
6.7%
t15848
6.7%
s15848
6.7%
p15848
6.7%
e15848
6.7%
m15848
6.7%
Other values (2)31696
13.3%
Common
ValueCountFrequency (%)
31696
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII269416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r31696
11.8%
31696
11.8%
i31696
11.8%
l31696
11.8%
P15848
 
5.9%
a15848
 
5.9%
t15848
 
5.9%
s15848
 
5.9%
p15848
 
5.9%
e15848
 
5.9%
Other values (3)47544
17.6%

CO Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct446
Distinct (%)0.028142352347299344
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.3534710248611812
Minimum0.0
Maximum1.3625
Zeros4
Zeros (%)0.0002523977788995457
Memory size126912
2022-10-20T18:51:06.905291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts15848
33.3%
per15848
33.3%
million15848
33.3%

Most occurring characters

ValueCountFrequency (%)
r31696
11.8%
31696
11.8%
i31696
11.8%
l31696
11.8%
P15848
 
5.9%
a15848
 
5.9%
t15848
 
5.9%
s15848
 
5.9%
p15848
 
5.9%
e15848
 
5.9%
Other values (3)47544
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter221872
82.4%
Space Separator31696
 
11.8%
Uppercase Letter15848
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r31696
14.3%
i31696
14.3%
l31696
14.3%
a15848
7.1%
t15848
7.1%
s15848
7.1%
p15848
7.1%
e15848
7.1%
m15848
7.1%
o15848
7.1%
Space Separator
ValueCountFrequency (%)
31696
100.0%
Uppercase Letter
ValueCountFrequency (%)
P15848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin237720
88.2%
Common31696
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r31696
13.3%
i31696
13.3%
l31696
13.3%
P15848
6.7%
a15848
6.7%
t15848
6.7%
s15848
6.7%
p15848
6.7%
e15848
6.7%
m15848
6.7%
Other values (2)31696
13.3%
Common
ValueCountFrequency (%)
31696
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII269416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r31696
11.8%
31696
11.8%
i31696
11.8%
l31696
11.8%
P15848
 
5.9%
a15848
 
5.9%
t15848
 
5.9%
s15848
 
5.9%
p15848
 
5.9%
e15848
 
5.9%
Other values (3)47544
17.6%

CO Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct446
Distinct (%)0.028142352347299344
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.3534710248611812
Minimum0.0
Maximum1.3625
Zeros4
Zeros (%)0.0002523977788995457
Memory size126912
2022-10-20T19:29:55.219969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.166667
Q10.241667
median0.316667
Q30.429167
95-th percentile0.6625
Maximum1.3625
Range1.3625
Interquartile range (IQR)0.1875

Descriptive statistics

Standard deviation0.1601145415
Coefficient of variation (CV)0.4529778405
Kurtosis2.835511529
Mean0.3534710249
Median Absolute Deviation (MAD)0.0875
Skewness1.391860023
Sum5601.808802
Variance0.02563666641
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.540132146 × 10-9
2022-10-20T18:51:07.084755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.166667
Q10.241667
median0.316667
Q30.429167
95-th percentile0.6625
Maximum1.3625
Range1.3625
Interquartile range (IQR)0.1875

Descriptive statistics

Standard deviation0.1601145415
Coefficient of variation (CV)0.4529778405
Kurtosis2.835511529
Mean0.3534710249
Median Absolute Deviation (MAD)0.0875
Skewness1.391860023
Sum5601.808802
Variance0.02563666641
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.540132146 × 10-9
2022-10-20T19:29:55.318814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2444
 
2.8%
0.266667290
 
1.8%
0.3268
 
1.7%
0.241667262
 
1.7%
0.2625240
 
1.5%
0.233333226
 
1.4%
0.254167218
 
1.4%
0.220833216
 
1.4%
0.258333212
 
1.3%
0.229167208
 
1.3%
Other values (436)13264
83.7%
ValueCountFrequency (%)
04
< 0.1%
0.0041672
< 0.1%
0.0083334
< 0.1%
0.0166672
< 0.1%
0.0208332
< 0.1%
0.0252
< 0.1%
0.0291672
< 0.1%
0.03754
< 0.1%
0.0416674
< 0.1%
0.0458334
< 0.1%
ValueCountFrequency (%)
1.36252
< 0.1%
1.3416672
< 0.1%
1.31252
< 0.1%
1.1791672
< 0.1%
1.1752
< 0.1%
1.1708332
< 0.1%
1.1666672
< 0.1%
1.1565222
< 0.1%
1.1208332
< 0.1%
1.11252
< 0.1%
2022-10-20T18:51:07.508792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2444
 
2.8%
0.266667290
 
1.8%
0.3268
 
1.7%
0.241667262
 
1.7%
0.2625240
 
1.5%
0.233333226
 
1.4%
0.254167218
 
1.4%
0.220833216
 
1.4%
0.258333212
 
1.3%
0.229167208
 
1.3%
Other values (436)13264
83.7%
ValueCountFrequency (%)
04
< 0.1%
0.0041672
< 0.1%
0.0083334
< 0.1%
0.0166672
< 0.1%
0.0208332
< 0.1%
0.0252
< 0.1%
0.0291672
< 0.1%
0.03754
< 0.1%
0.0416674
< 0.1%
0.0458334
< 0.1%
ValueCountFrequency (%)
1.36252
< 0.1%
1.3416672
< 0.1%
1.31252
< 0.1%
1.1791672
< 0.1%
1.1752
< 0.1%
1.1708332
< 0.1%
1.1666672
< 0.1%
1.1565222
< 0.1%
1.1208332
< 0.1%
1.11252
< 0.1%
2022-10-20T19:29:55.592942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Value
Real number (ℝ≥0)

HIGH CORRELATION

Distinct47
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7033821302
Minimum0
Maximum5.4
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size123.9 KiB
2022-10-20T18:51:07.757960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Value
Real number (ℝ≥0)

HIGH CORRELATION

Distinct47
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7033821302
Minimum0
Maximum5.4
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size123.9 KiB
2022-10-20T19:29:55.740155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.4
median0.6
Q30.825
95-th percentile1.7
Maximum5.4
Range5.4
Interquartile range (IQR)0.425

Descriptive statistics

Standard deviation0.5055268913
Coefficient of variation (CV)0.7187087496
Kurtosis9.889272091
Mean0.7033821302
Median Absolute Deviation (MAD)0.2
Skewness2.511999602
Sum11147.2
Variance0.2555574379
MonotonicityNot monotonic
2022-10-20T18:51:07.934621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.4
median0.6
Q30.825
95-th percentile1.7
Maximum5.4
Range5.4
Interquartile range (IQR)0.425

Descriptive statistics

Standard deviation0.5055268913
Coefficient of variation (CV)0.7187087496
Kurtosis9.889272091
Mean0.7033821302
Median Absolute Deviation (MAD)0.2
Skewness2.511999602
Sum11147.2
Variance0.2555574379
MonotonicityNot monotonic
2022-10-20T19:29:55.864413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.42318
14.6%
0.32318
14.6%
0.52138
13.5%
0.61724
10.9%
0.71326
8.4%
0.81064
6.7%
0.2960
6.1%
0.9786
 
5.0%
1550
 
3.5%
1.1512
 
3.2%
Other values (37)2152
13.6%
ValueCountFrequency (%)
04
 
< 0.1%
0.134
 
0.2%
0.2960
6.1%
0.32318
14.6%
0.42318
14.6%
0.52138
13.5%
0.61724
10.9%
0.71326
8.4%
0.81064
6.7%
0.9786
 
5.0%
ValueCountFrequency (%)
5.44
< 0.1%
52
 
< 0.1%
4.82
 
< 0.1%
4.42
 
< 0.1%
4.32
 
< 0.1%
4.22
 
< 0.1%
4.12
 
< 0.1%
3.92
 
< 0.1%
3.86
< 0.1%
3.74
< 0.1%

CO 1st Max Hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.268929833
Minimum0
Maximum23
Zeros4740
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size123.9 KiB
2022-10-20T18:51:08.071667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.42318
14.6%
0.32318
14.6%
0.52138
13.5%
0.61724
10.9%
0.71326
8.4%
0.81064
6.7%
0.2960
6.1%
0.9786
 
5.0%
1550
 
3.5%
1.1512
 
3.2%
Other values (37)2152
13.6%
ValueCountFrequency (%)
04
 
< 0.1%
0.134
 
0.2%
0.2960
6.1%
0.32318
14.6%
0.42318
14.6%
0.52138
13.5%
0.61724
10.9%
0.71326
8.4%
0.81064
6.7%
0.9786
 
5.0%
ValueCountFrequency (%)
5.44
< 0.1%
52
 
< 0.1%
4.82
 
< 0.1%
4.42
 
< 0.1%
4.32
 
< 0.1%
4.22
 
< 0.1%
4.12
 
< 0.1%
3.92
 
< 0.1%
3.86
< 0.1%
3.74
< 0.1%

CO 1st Max Hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.268929833
Minimum0
Maximum23
Zeros4740
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size123.9 KiB
2022-10-20T19:29:55.985721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q320
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.857152979
Coefficient of variation (CV)0.9555744987
Kurtosis-1.457297684
Mean9.268929833
Median Absolute Deviation (MAD)7
Skewness0.4317380918
Sum146894
Variance78.4491589
MonotonicityNot monotonic
2022-10-20T18:51:08.202902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q320
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.857152979
Coefficient of variation (CV)0.9555744987
Kurtosis-1.457297684
Mean9.268929833
Median Absolute Deviation (MAD)7
Skewness0.4317380918
Sum146894
Variance78.4491589
MonotonicityNot monotonic
2022-10-20T19:29:56.076082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
04740
29.9%
61614
 
10.2%
71544
 
9.7%
221290
 
8.1%
231124
 
7.1%
211026
 
6.5%
20844
 
5.3%
5618
 
3.9%
19576
 
3.6%
8564
 
3.6%
Other values (14)1908
12.0%
ValueCountFrequency (%)
04740
29.9%
1522
 
3.3%
2220
 
1.4%
384
 
0.5%
468
 
0.4%
5618
 
3.9%
61614
 
10.2%
71544
 
9.7%
8564
 
3.6%
9188
 
1.2%
ValueCountFrequency (%)
231124
7.1%
221290
8.1%
211026
6.5%
20844
5.3%
19576
3.6%
18296
 
1.9%
1792
 
0.6%
1636
 
0.2%
1528
 
0.2%
146
 
< 0.1%

CO AQI
Numeric time series

HIGH CORRELATION
MISSING
NON STATIONARY
SEASONAL

Distinct26
Distinct (%)0.003281171125694094
Missing7924
Missing (%)0.5
Infinite0
Infinite (%)0.0
Mean6.272841998990409
Minimum0.0
Maximum31.0
Zeros2
Zeros (%)0.00012619888944977284
Memory size126912
2022-10-20T18:51:08.332029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
04740
29.9%
61614
 
10.2%
71544
 
9.7%
221290
 
8.1%
231124
 
7.1%
211026
 
6.5%
20844
 
5.3%
5618
 
3.9%
19576
 
3.6%
8564
 
3.6%
Other values (14)1908
12.0%
ValueCountFrequency (%)
04740
29.9%
1522
 
3.3%
2220
 
1.4%
384
 
0.5%
468
 
0.4%
5618
 
3.9%
61614
 
10.2%
71544
 
9.7%
8564
 
3.6%
9188
 
1.2%
ValueCountFrequency (%)
231124
7.1%
221290
8.1%
211026
6.5%
20844
5.3%
19576
3.6%
18296
 
1.9%
1792
 
0.6%
1636
 
0.2%
1528
 
0.2%
146
 
< 0.1%

CO AQI
Numeric time series

HIGH CORRELATION
MISSING
NON STATIONARY
SEASONAL

Distinct26
Distinct (%)0.003281171125694094
Missing7924
Missing (%)0.5
Infinite0
Infinite (%)0.0
Mean6.272841998990409
Minimum0.0
Maximum31.0
Zeros2
Zeros (%)0.00012619888944977284
Memory size126912
2022-10-20T19:29:56.151261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median6
Q38
95-th percentile13
Maximum31
Range31
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.54178668
Coefficient of variation (CV)0.5646223323
Kurtosis4.088942781
Mean6.272841999
Median Absolute Deviation (MAD)2
Skewness1.53839853
Sum49706
Variance12.54425288
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.845126501 × 10-5
2022-10-20T18:51:08.494935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median6
Q38
95-th percentile13
Maximum31
Range31
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.54178668
Coefficient of variation (CV)0.5646223323
Kurtosis4.088942781
Mean6.272841999
Median Absolute Deviation (MAD)2
Skewness1.53839853
Sum49706
Variance12.54425288
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.845126501 × 10-5
2022-10-20T19:29:56.236011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
31536
 
9.7%
51396
 
8.8%
61188
 
7.5%
7842
 
5.3%
2710
 
4.5%
8674
 
4.3%
9472
 
3.0%
10344
 
2.2%
13184
 
1.2%
11180
 
1.1%
Other values (16)398
 
2.5%
(Missing)7924
50.0%
ValueCountFrequency (%)
02
 
< 0.1%
124
 
0.2%
2710
4.5%
31536
9.7%
51396
8.8%
61188
7.5%
7842
5.3%
8674
4.3%
9472
 
3.0%
10344
 
2.2%
ValueCountFrequency (%)
314
 
< 0.1%
272
 
< 0.1%
262
 
< 0.1%
252
 
< 0.1%
244
 
< 0.1%
238
 
0.1%
2216
0.1%
2018
0.1%
1912
 
0.1%
1838
0.2%
2022-10-20T18:51:08.826888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
31536
 
9.7%
51396
 
8.8%
61188
 
7.5%
7842
 
5.3%
2710
 
4.5%
8674
 
4.3%
9472
 
3.0%
10344
 
2.2%
13184
 
1.2%
11180
 
1.1%
Other values (16)398
 
2.5%
(Missing)7924
50.0%
ValueCountFrequency (%)
02
 
< 0.1%
124
 
0.2%
2710
4.5%
31536
9.7%
51396
8.8%
61188
7.5%
7842
5.3%
8674
4.3%
9472
 
3.0%
10344
 
2.2%
ValueCountFrequency (%)
314
 
< 0.1%
272
 
< 0.1%
262
 
< 0.1%
252
 
< 0.1%
244
 
< 0.1%
238
 
0.1%
2216
0.1%
2018
0.1%
1912
 
0.1%
1838
0.2%
2022-10-20T19:29:56.403024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

Interactions

2022-10-20T18:50:51.363641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

Interactions

2022-10-20T19:29:46.348312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:50:50.454011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:29:45.799871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:50:50.916614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:29:46.094666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:50:51.511716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:29:46.431009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:50:50.623101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:29:45.920048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:50:51.064698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:29:46.189052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:50:51.650470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:29:46.507003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:50:50.782367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:29:46.010635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:50:51.214604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:29:46.268655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-20T18:51:09.078695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/2022-10-20T19:29:56.540686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-10-20T18:51:09.296924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-10-20T19:29:56.695614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-20T18:51:09.603259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-20T19:29:56.845243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-20T18:51:09.882853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-20T19:29:56.994747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-20T18:51:10.134492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-20T19:29:57.129937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-20T18:51:10.319862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-20T19:29:57.238870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-20T18:50:51.956394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-20T19:29:46.684077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-20T18:50:52.601153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-20T19:29:47.047071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-20T18:50:52.945483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-20T19:29:47.234989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-20T18:50:53.107184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-20T19:29:47.338845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
041910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-01Parts per billion15.20833338.01936Parts per million0.0229170.039933Parts per billion1.8333334.086.0Parts per million0.3833331.319NaN
141910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-01Parts per billion15.20833338.01936Parts per million0.0229170.039933Parts per billion1.8333334.086.0Parts per million0.3210530.7238.0
241910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-01Parts per billion15.20833338.01936Parts per million0.0229170.039933Parts per billion1.8125002.32NaNParts per million0.3833331.319NaN
341910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-01Parts per billion15.20833338.01936Parts per million0.0229170.039933Parts per billion1.8125002.32NaNParts per million0.3210530.7238.0
441910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-02Parts per billion18.50000038.01936Parts per million0.0157080.0311026Parts per billion1.3750002.073.0Parts per million0.3312500.78NaN
541910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-02Parts per billion18.50000038.01936Parts per million0.0157080.0311026Parts per billion1.3750002.073.0Parts per million0.4388890.819.0
641910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-02Parts per billion18.50000038.01936Parts per million0.0157080.0311026Parts per billion1.3625002.011NaNParts per million0.3312500.78NaN
741910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-02Parts per billion18.50000038.01936Parts per million0.0157080.0311026Parts per billion1.3625002.011NaNParts per million0.4388890.819.0
841910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-04Parts per billion31.52173942.01040Parts per million0.0134170.023819Parts per billion2.3333334.0186.0Parts per million0.9250001.919NaN
941910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-04Parts per billion31.52173942.01040Parts per million0.0134170.023819Parts per billion2.3333334.0186.0Parts per million1.0142861.12013.0

Last rows

State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
1583841910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-29Parts per billion12.93478330.5028Parts per million0.0299580.0382032Parts per billion0.3304350.690.0Parts per million0.2260870.68NaN
1583941910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-29Parts per billion12.93478330.5028Parts per million0.0299580.0382032Parts per billion0.3304350.690.0Parts per million0.3250000.708.0
1584041910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-30Parts per billion10.85416738.22136Parts per million0.0265000.037931Parts per billion0.2125000.35NaNParts per million0.2291670.521NaN
1584141910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-30Parts per billion10.85416738.22136Parts per million0.0265000.037931Parts per billion0.2125000.35NaNParts per million0.1916670.4235.0
1584241910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-30Parts per billion10.85416738.22136Parts per million0.0265000.037931Parts per billion0.2500000.330.0Parts per million0.2291670.521NaN
1584341910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-30Parts per billion10.85416738.22136Parts per million0.0265000.037931Parts per billion0.2500000.330.0Parts per million0.1916670.4235.0
1584441910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-31Parts per billion19.09166731.2729Parts per million0.0186840.0301125Parts per billion0.2666670.4140.0Parts per million0.3500000.405.0
1584541910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-31Parts per billion19.09166731.2729Parts per million0.0186840.0301125Parts per billion0.2500000.38NaNParts per million0.3333330.87NaN
1584641910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-31Parts per billion19.09166731.2729Parts per million0.0186840.0301125Parts per billion0.2666670.4140.0Parts per million0.3333330.87NaN
1584741910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-31Parts per billion19.09166731.2729Parts per million0.0186840.0301125Parts per billion0.2500000.38NaNParts per million0.3500000.405.0